Techniques for reordering words of sentences for improved translation between languages

ABSTRACT

Computer-implemented techniques include receiving a phrase in a first language and obtaining a corpus comprising a plurality of phrases in the first language and word reordering information for the plurality of phrases, the word reordering information indicating a correct word order for each phrase in a second language. Word-to-word correspondences between each of the phrases in the first language and the corresponding correct word order for the phrase in the second language are identified and at least one tree that allows for the identified word-to-word correspondences is generated. Based upon the at least one tree, a statistical model for reordering from a word order that is correct for the first language to a word order that is correct for the second language is created. Based upon the statistical model, a reordered phrase from the received phrase is generated, the reordered phrase having a correct word order for the second language.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.61/469,426, filed Mar. 30, 2011, the disclosure of which is incorporatedby reference in its entirety.

BACKGROUND

Different languages may use different standard word orders inconventional sentence structures. For example, English typically uses asubject-verb-object sentence order, while German may use a differentword order resulting from a preference for the verb to be the secondword in each sentence. As another example, Japanese typically uses asubject-object-verb sentence structure.

When translating from one language to another with automated techniquessuch as machine translation, it may be necessary to identify and accountfor differences in sentence structure or syntax, i.e., the order inwhich words typically are placed in a sentence. If these differences arenot accounted for, the translation may be inaccurate or have a differentimplied or explicit meaning from the original source sentence. Forexample, mechanically translating from a subject-verb-object language toa subject-object-verb language may result in a mis-translation, if theverb is not moved to the correct position in the target language. Thus,the target sentence may be read incorrectly or may be partially orcompletely nonsensical or confusing in meaning. An incorrect move alsomay impact the effectiveness of other models, such as a related languagemodel, which may negatively impact fluency and translation accuracy.

To address this issue, machine translation systems may use pre-orderingtechniques when translating between languages that use differentsentence structures. Pre-ordering techniques attempt to rearrange asource sentence to match the target language structure, prior totranslating the individual tokens in the source sentence. Someconventional pre-ordering techniques use a supervised parser to achievean accurate ordering. Generally, supervised parsers include systems thatautomatically annotate sentences with their syntactic structure, basedon human-generated annotations of syntactic structure on trainingexamples. Other conventional pre-ordering techniques may attempt tore-order without the use of any parser.

BRIEF SUMMARY

Methods and systems according to an embodiment of the disclosed subjectmatter may provide for reordering of a source phrase for translation. Inan embodiment, a phrase to be translated from a first language to asecond language may be obtained. A corpus that includes a set of phrasesin the first language and example translations of the set of phrases inthe second language also may be obtained. The corpus may exclude atleast a portion of the phrase to be translated. For each phrase pair inthe corpus, a word-to-word correspondence between words in the phrase inthe first language and the example translations of the phrase in thesecond language may be determined. Based upon the correspondences, atree structure of the phrase in the first language, such as a binarytree, may be generated, where each node in the tree represents one ormore words in the phrase and the tree maintains the detectedword-to-word correspondence. A statistical reordering model based uponthe trees may then be constructed for the corpus. The statisticalreordering model may define a word reordering for the phrase to betranslated such that applying the model to the phrase to be translatedresults in a re-ordered phrase suitable for the second language. Thereordered phrase may be provided to a machine translator to translatethe reordered phrase into the second language

According to an embodiment, a phrase in a first language may bereceived. The phrase may be provided in a request from a user or othersource. In an embodiment, the phrase may originate as part of a requestto translate the phrase from the first language into a second language.A corpus obtained that includes a plurality of phrases in the firstlanguage and word reordering information for the plurality of phrasesalso may be received or otherwise obtained. The word reorderinginformation may indicate a correct word order for each phrase in thecorpus when translated to a second language. Word-to-wordcorrespondences between each of the phrases in the first language andthe corresponding correct word order for the phrase in the secondlanguage may then be identified, thus allowing for generation of atleast one tree that allows for the identified word-to-wordcorrespondences. Such trees may be generated by, for example, generatinga plurality of trees, not all of which allow for the correspondences,and selecting one or more that properly allow for the identifiedword-to-word correspondences. A statistical model for reordering from aword order that is correct for the first language to a word order thatis correct for the second language may be generated based upon the tree.In an embodiment, the process of generating the statistical model mayinclude generating a statistical model of tree generation for phrases inthe first language based upon the phrases in the corpus. The receivedphrase may then be reordered according to the model, where the reorderedphrase has a correct word order for the second language. An embodimentalso may generate a translation of the received phrase in the secondlanguage based upon the reordered phrase. Various machine learningtechniques may be used to generate the statistical model.

A system according to an embodiment may include an input configured toreceive a phrase in a first language, a computer-readable storage mediumstoring a corpus comprising a plurality of phrases in the first languageand word reordering information for the plurality of phrases, where theword reordering information indicates a correct word order for eachphrase in a second language; and a processor configured to obtain thecorpus from the computer-readable storage medium, identify word-to-wordcorrespondences between each of the phrases in the first language andthe corresponding correct word order for the phrase in the secondlanguage, generate at least one tree that allows for the identifiedword-to-word correspondences, based upon the at least one tree, create astatistical model for reordering from a word order that is correct forthe first language to a word order that is correct for the secondlanguage, and, based upon the statistical model, generating a reorderedphrase from the received phrase, the reordered phrase having a correctword order for the second language.

Other features may be present in embodiments of the presently disclosedsubject matter. For example, at least one tree may be a binary tree. Atranslation of the received phrase in the second language may begenerated based upon the reordered phrase. A process of creating thestatistical model may include generating a statistical model of treegeneration for phrases in the first language based upon the phrases inthe corpus. Techniques disclosed herein also may include receiving arequest to translate the received phrase from the first language to thesecond language. Statistical model as disclosed may be generated using amachine learning technique, such as a probabilistic machine learningtechnique, a quasi-Newtonian machine learning technique, a margin-basedmachine learning technique, an online machine learning technique, or acombination thereof. The process of generating a tree may includegenerating a plurality of trees, at least one of which does not allowfor the identified word-to-word correspondences; and selecting, from thegenerated trees, at least one tree. Word reordering information asdisclosed herein may include known-good translations of the phrases inthe corpus in the second language.

Additional features, advantages, and embodiments of the disclosedsubject matter may be set forth or apparent from consideration of thefollowing detailed description, drawings, and claims. Moreover, it is tobe understood that both the foregoing summary and the following detaileddescription are exemplary and are intended to provide furtherexplanation without limiting the scope of the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are included to provide a furtherunderstanding of the disclosed subject matter, are incorporated in andconstitute a part of this specification. The drawings also illustrateembodiments of the disclosed subject matter and together with thedetailed description serve to explain the principles of embodiments ofthe disclosed subject matter. No attempt is made to show structuraldetails in more detail than may be necessary for a fundamentalunderstanding of the disclosed subject matter and various ways in whichit may be practiced.

FIG. 1A shows a computer according to an embodiment of the disclosedsubject matter.

FIG. 1B shows a network configuration according to an embodiment of thedisclosed subject matter.

FIG. 2 shows an example process for reordering a phrase according to anembodiment of the disclosed subject matter.

FIGS. 3A-3C show example word-to-word correspondences between a sourcephrase in a word order suitable for a first language, and a word ordersuitable for an illustrative second language.

FIGS. 4A and 4B show example schematic representations of a parse treeaccording to an embodiment of the disclosed subject matter.

FIG. 5 shows an example of a process for generating and applying areordering model according to an embodiment of the disclosed subjectmatter.

DETAILED DESCRIPTION

When translating between languages that differ substantially in wordorder, machine translation systems may benefit from techniques thatpermute source tokens, such as words in a source sentence to betranslated into a target sentence in a different language, into anordering suitable for the target language based on a syntactic parse ofthe input. Embodiments of the presently disclosed subject matter providetechniques for hierarchical pre-ordering over induced parses. Thetechniques may be generated or learned automatically from a parallelcorpus of example sentence structure, without the need for aconventional supervised parser, such as one that is trained on atreebank.

Statistical machine translation models generally define weightedmappings from source to target sentences, where the source sentence isin a first source language and the target sentence is in a second,different, target language. Such models may include three generalcomponents: a target-side language model, a lexical transfer model, anda distortion or reordering model.

Embodiments of the presently-disclosed subject matter provide techniquesthat may permute source sentences into a target-like order beforeapplying the transfer or language models. The reordering techniques maybe applied at both training and/or testing time, and may be dependent orconditioned on source-side syntactic structure. Techniques disclosedherein may be particularly effective at enforcing tree-rotatingtransformations, such as an ordering change from a subject-verb-objectlanguage such as English, to a subject-object-verb language such asJapanese. In contrast to conventional pre-ordering techniques that mayrequire a human-supervised parser, embodiments of the presentlydisclosed subject matter may use syntax induction techniques that learnboth a parsing model and a reordering model directly from a word-alignedparallel corpus, such as example translations from a source language toa target language. For example, a parallel corpus may include a set ofsentences in a first language and corresponding known-correcttranslations of the sentences to a second language. The exampletranslations may include known-correct translations, partially-correcttranslations, partially-incorrect or incorrect translations, andtranslations of unknown correctness. Generally, techniques disclosedherein may perform acceptably well even if some example translations inthe corpus are incorrect, and/or if the majority of the exampletranslations are correct. Techniques disclosed herein may belanguage-independent and may not require syntactic annotations or otherhuman input, while still successfully predicting reordering phenomena.

Embodiments of the presently disclosed subject matter may reorder sourcesentences in two stages: parsing and tree reordering. In the firststage, a tree structure over the token sequence, such as abinary-branching parse tree, may be inferred. The parse tree may permit,for example, a particular subset of every possible reordering of thesource sentence. As an example, only bracketings within a parse treethat properly separate main verbs from their object noun phrases mayallow for a transformation needed to convert a subject-verb-object (SVO)order into a subject-object-verb (SOV) order.

In a second stage, a word reordering may be selected by inverting theorder of some subset of binary productions in a parse tree. Thereordering component may emphasize monolingual parsing as a first phase,and may select reorderings without the aid of a target-side languagemodel. That is, the reordering may be performed without having accessto, or first constructing, a complete model of the language to whichsource text is to be translated.

To train a parsing model for reordering, a learning criterion may beselected. Rather than inducing grammatical structure by maximizinglikelihood of an observed sequence of words or other tokens, a structuremay be selected that is effective for reordering. This may focus theexplanatory power of the model on phenomena of interest for machinetranslation. For example, the conditional likelihood of source-sideprojections of synchronous parses of word aligned parallel sentences maybe maximized. This may provide a feature-rich, log-linear parsing modelwithout requiring supervised treebank data.

More generally, embodiments of the presently disclosed subject mattermay examine example translations from a first language to a secondlanguage to construct syntactic structure, such as word grouping orhierarchy. A set of example translations from a particular firstlanguage to a particular second language may be referred to as atranslation corpus. This structure then may be used as the basis for amodel that reorders words in the first language to an order that is moresuited to a second language. The reordering may be performed before anytranslation or other transformation to the second language is performed.That is, text in a first language may be kept in the first languageuntil after the words in the text have been reordered. This may be an“unsupervised” process, i.e., requiring minimal or no human oversight toobtain a word order that is appropriate to the second language.Embodiments of the presently disclosed subject matter typically areapplied to sentences or larger collections of text, but more generallymay be applied to any size phrase that is to be translated from onelanguage to another.

FIG. 2 shows an example process for reordering a phrase, such as priorto translating the phrase from its initial language to another language,according to an embodiment of the disclosed subject matter. At 210,word-to-word correspondences may be identified between one or morephrases in a corpus, and the corresponding translation of the phrase inthe corpus. As described above, a “corpus” may refer to a set of exampletranslations, which may include known-good translations, between a firstlanguage and a second language. More generally, a “corpus” also maycontain a set of phrases in a first language, and word order informationfor those phrases in the first and/or a second language. As a specificexample, a corpus may include a set of sentences in English and thecorresponding correct translations of the sentences in Japanese.Examples of word-to-word correspondences and their use are described infurther detail in relation to FIGS. 3 and 4. The word-to-wordcorrespondences may identify the position of each word and itscorresponding translation within the original (first language) text andthe translated (second language) text.

After identifying the word-to-word correspondences, one or more treesmay be constructed at 220 for each phrase in the corpus. As will beunderstood by one of skill in the art, a tree may represent a set ofpotential word orders of the phrase in the first language, so the treemay be used to model a set of potential reorderings of the phrase.Typically, not every possible tree will be constructed or selected;rather, only trees that allow for the word-to-word correspondenceidentified for the phrase at 210 will be generated. For example, if itis determined from the word-to-word correspondence for a particularphrase and its translation that the first three words in the phrase inthe first language become the last three words in the phrase in thesecond language, only trees that allow the first three words to bereordered to become the last three words will be constructed orselected. In some cases only those trees that allow for the identifiedword-to-word correspondence will be generated. In other cases it may bemore desirable to generate each possible tree and select only those thatallow for the correspondences. Although described and illustratedprimarily with respect to binary trees, it will be understood that thetechniques and systems disclosed herein may be used with any othersuitable tree structure, including non-binary trees.

A tree structure may be generated for multiple pairs of phrases in thecorpus to create a set of trees, each of which allows for theword-to-word correspondences identified at 210. Thus, a set of one ormore trees may be generated for the corpus. The set of trees then mayprovide input to a machine learning process to create a statisticalmodel that is capable of predicting a tree for a phrase in the firstlanguage that will allow for a correct reordering suitable for thesecond language, without relying upon the availability of the phrase inthe second language.

At 230, a statistical model is generated that can predict the set oftrees for the corpus, without using the example translations or the wordorder of the example translations, for each phrase. That is, the modelis constructed to be capable of predicting an appropriate tree for aphrase in the corpus based only upon the phrase in the original firstlanguage. Because the model is based upon the trees generated at 220, italso allows only for those reorderings that maintain the word-to-wordcorrespondences identified at 210. Thus, the word-to-wordcorrespondences may be used to constrain the model to an appropriatesubset of all possible reordering structures.

The statistical model generated at 230 may be created using any suitablemachine learning technique. For example, a machine learning algorithmmay be used that creates a function that depends upon the words, wordpairs, punctuation, numerical values, or other attributes of each phrasein the first language. The function for each possible tree may beassigned a ranking score, where high scores are assigned to trees thatallow for the word-to-word correspondences identified at 210, and lowscores are assigned to trees that do not. Other features of the corpusphrases may be used to score the trees. For example, statisticsregarding the frequency of various words occurring next to or near eachother; the degree of variability of words that follow or precede a word,which may be scored relative to the previous n words in the phrase;comparison to other supervised or unsupervised grammar rules for thesource phrase in the first language; and other statistical values may beused. Other attributes and features of the phrase in the target languageor the function may be used in ranking the tree, as will be readilyunderstood by one of skill in the art. Specific examples of machinelearning techniques that may be suitable for generating the statisticalmodel may include probabilistic, quasi-Newtonian, margin-based, and/oronline techniques, specific examples of which may include a regularizedconditional likelihood maximization via quasi-Newton methods, structuredsupport vector machines, and structured perceptron techniques. Variousother machine learning techniques may be used.

Similar to the model generated at 230, at 240 a model for reordering thetrees may be generated or learned. For example, various reorderings maybe generated and compared to the word order of example translations inthe corpus. Those reorderings that have attributes which lead to acorrect reordering may be scored relatively high, while those that donot may be scored relatively low. Attributes of the reorderings may beconsidered when assigning scores. For example, if a tree allows foradjectives to be reordered such that they follow the nouns they modifyinstead of preceding the nouns they modify, and this attribute matchesthe second language in the corpus, this attribute may give a positivecontribution to the reordering score. If this is not a desirable featurefor the second language, it may give a negative contribution to thescore. Other attributes and features may be considered and, as disclosedwith respect to 230, any suitable machine learning technique may be usedto learn the correct reordering for one or more phrases in the corpus.

FIGS. 3A-3C show example word-to-word correspondences between a sourcephrase in a word order suitable for a first language (“The quick brownfox jumps over the lazy dog”), and a word order suitable for anillustrative second language. It will be understood that the illustratedword orders are intended as examples only, and may not correspond to aword order suitable for any specific real language. As shown by FIGS.3A-3C, the word-to-word correspondences may provide information aboutpatterns in word reordering between the first and second language for aparticular corpus. For example, FIG. 3A shows a phrase from a corpus inwhich the first language (top) is a SVO language in which adjectivesprecede nouns, and the second language (bottom) is a SVO language inwhich adjectives follow nouns. In FIG. 3A, the words associated with thesubject portion of the phrase do not shift to a different general partof the phrase, such as the middle or end. As another example, FIG. 3Bshows a phrase pair from a corpus in which the second language is a VOS(verb-object-subject) language. As shown, the subject and objectportions can be said generally to swap positions between the first andsecond language word orders. FIG. 3C shows another example in which thesecond language is a verb-subject-object language; as shown, the objectportions of the phrase do not shift during an appropriate reordering.

The phrase pairs shown in FIGS. 3A-3C may represent example wordreorderings for given first and second languages. As previouslydescribed, a parse tree for each phrase that allows for the illustratedreorderings may be constructed or selected from among a series ofpossible parse trees.

FIGS. 4A and 4B show example schematic representations of such a parsetree. It will be understood that the specific arrangement andrepresentation shown in FIGS. 4A-4B are illustrative only, and thatother types and representations of parse trees may be used. As anexample, FIG. 4A shows one possible parse tree for the phraseillustrated in FIGS. 3A-3C, “The quick brown fox jumps over the lazydog”. In general, a parse tree allows for certain reorderings of thewords in the phrase, and disallows others. For example, the treestructure shown in FIG. 4A allows for word orders that may be formed byswapping branches of the tree, while disallowing those that cannot beformed by swapping branches. As a specific example, FIG. 4A allows forthe word order “over jumps the lazy dog the quick brown fox”. To achievethis order, the nodes of the parse tree may be rearranged by swappingleft and right branches to arrive at the arrangement shown in FIG. 4B.As another example, the word order “jumps over the quick brown fox thelazy dog” is now allowed by the tree structure shown in FIG. 4A, becausethere is no set of node exchanges that will result in a tree that hasthat word order.

Reordering models learned from corpora as disclosed herein may be usedduring machine translation of a phrase from the first language to thesecond language in which phrases are stored in the corpora. That is, acorpus that includes word order information for phrases in a first andsecond language may be used to generate reordering models as describedherein, which may then be used to translate phrases from the firstlanguage to the second language. The translated phrases typically willbe phrases not included in the corpus, though they may include words,word combinations, or phrases that are in the corpus.

FIG. 5 shows an example of a more detailed process for generating andapplying a reordering model according to an embodiment of the disclosedsubject matter. At 510, a phrase to be translated from a first languageto a second language may be obtained. For example, the phrase may bereceived from a user, a preprocessor, or any other suitable source. Thephrase may be received before or after one or more reordering modelshave been created as disclosed herein and as illustrated in FIG. 5. Acorpus associated with the first and second languages may be accessed at520. The corpus may include pairs of phrases in the first language andtheir example translations in the second language, and/or word orderinformation for the phrase pairs. At 530, for each phrase pair, aword-to-word correspondence between words in the phrase in the firstlanguage and the example translations of the phrase in the secondlanguage may be determined. A tree then may be constructed for eachphrase in the first language at 540, where each node in the treerepresents one or more words in the phrase and the tree maintains thedetected word-to-word correspondence. As previously disclosed,word-to-word correspondences generally may be maintained by constructingor selecting only trees that allow for the correspondences whenreordering from the phrase in the first language to the word ordersuitable for the phrase in the second language.

As previously disclosed, at 550 a tree generation technique may begenerated based upon the trees constructed for the phrase pairs in thecorpus at 540. The tree generation technique may provide a mechanism togenerate a suitable tree for an arbitrary phrase in the first language.As previously described, it may be created by generating multiple trees,and verifying that the generated trees preserve the word-to-wordcorrespondences and/or other features that are desirable when reorderinga phrase for the second language. At 560, a statistical reordering modelmay be created based upon the trees. The model may define, for example,a word reordering for the phrase to be translated or an arbitrary phrasein the first language. The model may define a reordering for the phrasethat was received at 510 by including the reordering explicitly, or byproviding a function that can be used to accomplish the correctreordering or otherwise generate such a reordering definition. At 570,the tree generation model may be applied to a received phrase to betranslated. The reordering model may then be applied to the inferredtree at 580. The reordered phrase may then be provided to a translatorat 590, such as a machine translator, that is configured to translatethe reordered phrase into a phrase in the second language.

The attached appendix provides a detailed, non-limiting illustration ofan embodiment of the disclosed subject matter. For example, Section 2.2of the appendix describes in further detail an example of constructingparse trees that allow for the example reorderings in a corpus. Section2.3 similarly provides a non-limiting illustration of generating parsetrees that allow for example reorderings, without considering the sourcephrases in a corpus. Other features of embodiments of the disclosedsubject matter are also described in the appendix. For example, Section3.5 discloses a technique of separating a corpus into two portions, oneof which is used for machine learning of parse tree creation andreordering, and the second for verifying the learned trees andreordering techniques.

Embodiments of the presently disclosed subject matter may be implementedin and used with a variety of component and network architectures. FIG.1 is an example computer 20 suitable for implementing embodiments of thepresently disclosed subject matter. The computer 20 includes a bus 21which interconnects major components of the computer 20, such as acentral processor 24, a memory 27 (typically RAM, but which may alsoinclude ROM, flash RAM, or the like), an input/output controller 28, auser display 22, such as a display screen via a display adapter, a userinput interface 26, which may include one or more controllers andassociated user input devices such as a keyboard, mouse, and the like,and may be closely coupled to the I/O controller 28, fixed storage 23,such as a hard drive, flash storage, Fibre Channel network, SAN device,SCSI device, and the like, and a removable media component 25 operativeto control and receive an optical disk, flash drive, and the like.

The bus 21 allows data communication between the central processor 24and the memory 27, which may include read-only memory (ROM) or flashmemory (neither shown), and random access memory (RAM) (not shown), aspreviously noted. The RAM is generally the main memory into which theoperating system and application programs are loaded. The ROM or flashmemory can contain, among other code, the Basic Input-Output system(BIOS) which controls basic hardware operation such as the interactionwith peripheral components. Applications resident with the computer 20are generally stored on and accessed via a computer readable medium,such as a hard disk drive (e.g., fixed storage 23), an optical drive,floppy disk, or other storage medium 25.

The fixed storage 23 may be integral with the computer 20 or may beseparate and accessed through other interfaces. A network interface 29may provide a direct connection to a remote server via a telephone link,to the Internet via an internet service provider (ISP), or a directconnection to a remote server via a direct network link to the Internetvia a POP (point of presence) or other technique. The network interface29 may provide such connection using wireless techniques, includingdigital cellular telephone connection, Cellular Digital Packet Data(CDPD) connection, digital satellite data connection or the like. Forexample, the network interface 29 may allow the computer to communicatewith other computers via one or more local, wide-area, or othernetworks, as shown in FIG. 1B.

Many other devices or components (not shown) may be connected in asimilar manner (e.g., document scanners, digital cameras and so on).Conversely, all of the components shown in FIG. 1 need not be present topractice the present disclosure. The components can be interconnected indifferent ways from that shown. The operation of a computer such as thatshown in FIG. 1 is readily known in the art and is not discussed indetail in this application. Code to implement the present disclosure canbe stored in computer-readable storage media such as one or more of thememory 27, fixed storage 23, removable media 25, or on a remote storagelocation.

FIG. 1B shows an example network arrangement according to an embodimentof the disclosed subject matter. One or more clients 10, 11, such aslocal computers, smart phones, tablet computing devices, and the likemay connect to other devices via one or more networks 7. The network maybe a local network, wide-area network, the Internet, or any othersuitable communication network or networks, and may be implemented onany suitable platform including wired and/or wireless networks. Theclients may communicate with one or more servers 13 and/or databases 15.The devices may be directly accessible by the clients 10, 11, or one ormore other devices may provide intermediary access such as where aserver 13 provides access to resources stored in a database 15. Theclients 10, 11 also may access remote platforms 17 or services providedby remote platforms 17 such as cloud computing arrangements andservices. The remote platform 17 may include one or more servers 13and/or databases 15.

More generally, various embodiments of the presently disclosed subjectmatter may include or be embodied in the form of computer-implementedprocesses and apparatuses for practicing those processes. Embodimentsalso may be embodied in the form of a computer program product havingcomputer program code containing instructions embodied in non-transitoryand/or tangible media, such as floppy diskettes, CD-ROMs, hard drives,USB (universal serial bus) drives, or any other machine readable storagemedium, wherein, when the computer program code is loaded into andexecuted by a computer, the computer becomes an apparatus for practicingembodiments of the disclosed subject matter. Embodiments also may beembodied in the form of computer program code, for example, whetherstored in a storage medium, loaded into and/or executed by a computer,or transmitted over some transmission medium, such as over electricalwiring or cabling, through fiber optics, or via electromagneticradiation, wherein when the computer program code is loaded into andexecuted by a computer, the computer becomes an apparatus for practicingembodiments of the disclosed subject matter. When implemented on ageneral-purpose microprocessor, the computer program code segmentsconfigure the microprocessor to create specific logic circuits. In someconfigurations, a set of computer-readable instructions stored on acomputer-readable storage medium may be implemented by a general-purposeprocessor, which may transform the general-purpose processor or a devicecontaining the general-purpose processor into a special-purpose deviceconfigured to implement or carry out the instructions. Embodiments maybe implemented using hardware that may include a processor, such as ageneral purpose microprocessor and/or an Application Specific IntegratedCircuit (ASIC) that embodies all or part of the techniques according toembodiments of the disclosed subject matter in hardware and/or firmware.The processor may be coupled to memory, such as RAM, ROM, flash memory,a hard disk or any other device capable of storing electronicinformation. The memory may store instructions adapted to be executed bythe processor to perform the techniques according to embodiments of thedisclosed subject matter.

The foregoing description, for purpose of explanation, has beendescribed with reference to specific embodiments. However, theillustrative discussions above are not intended to be exhaustive or tolimit embodiments of the disclosed subject matter to the precise formsdisclosed. Many modifications and variations are possible in view of theabove teachings. The embodiments were chosen and described in order toexplain the principles of embodiments of the disclosed subject matterand their practical applications, to thereby enable others skilled inthe art to utilize those embodiments as well as various embodiments withvarious modifications as may be suited to the particular usecontemplated.

The invention claimed is:
 1. A computer-implemented method comprising:obtaining, at a computing device including one or more processors, aphrase to be translated from a first language to a second language; foreach phrase pair in a corpus, the corpus comprising a set of phrases inthe first language and example translations of the set of phrases in thesecond language, and excluding the phrase to be translated: determining,at the computing device, a word-to-word correspondence between words inthe phrase in the first language and the example translations of thephrase in the second language; and constructing, at the computingdevice, a tree of the phrase in the first language, each node in thetree representing one or more words in the phrase, the tree maintainingthe detected word-to-word correspondence; constructing, at the computingdevice, a statistical reordering model based upon the trees constructedfor the corpus, the statistical reordering model defining a wordreordering for the phrase to be translated; applying, at the computingdevice, the statistical reordering model to the phrase to be translatedto obtain a reordered phrase; and providing, at the computing device,the reordered phrase to a machine translator configured to translate thereordered phrase into a phrase in the second language.
 2. Acomputer-implemented method comprising: receiving, at a computing deviceincluding one or more processors, a phrase in a first language;obtaining, at the computing device, a corpus comprising a plurality ofphrases in the first language and word reordering information for theplurality of phrases, the word reordering information indicating acorrect word order for each phrase in a second language; identifying, atthe computing device, word-to-word correspondences between each of thephrases in the first language and the corresponding correct word orderfor the phrase in the second language; generating, at the computingdevice, at least one tree that allows for the identified word-to-wordcorrespondences; based upon the at least one tree, creating, at thecomputing device, a statistical model for reordering from a word orderthat is correct for the first language to a word order that is correctfor the second language; and based upon the statistical model,generating, at the computing device, a reordered phrase from thereceived phrase, the reordered phrase having a correct word order forthe second language.
 3. The method of claim 2, wherein each at least onetree is a binary tree.
 4. The method of claim 2, further comprising thestep of generating a translation of the received phrase in the secondlanguage based upon the reordered phrase.
 5. The method of claim 2,wherein the step of creating the statistical model further comprisesgenerating a statistical model of tree generation for phrases in thefirst language based upon the phrases in the corpus.
 6. The method ofclaim 2, further comprising receiving a request to translate thereceived phrase from the first language to the second language.
 7. Themethod of claim 2, wherein the statistical model is generated using amachine learning technique.
 8. The method of claim 7, wherein themachine learning technique comprises a probabilistic machine learningtechnique, a quasi-Newtonian machine learning technique, a margin-basedmachine learning technique, an online machine learning technique, or acombination thereof.
 9. The method of claim 2, wherein the step ofgenerating the at least one tree further comprises: generating aplurality of trees, at least one of which does not allow for theidentified word-to-word correspondences; and selecting, from theplurality of generated trees, the at least one tree.
 10. The method ofclaim 2, wherein the word reordering information comprises known-goodtranslations of the phrases in the corpus in the second language.
 11. Asystem comprising: an input configured to receive a phrase in a firstlanguage; a non-transitory computer-readable storage medium storing acorpus comprising a plurality of phrases in the first language and wordreordering information for the plurality of phrases, the word reorderinginformation indicating a correct word order for each phrase in a secondlanguage; and a processor configured to: obtain the corpus from thecomputer-readable storage medium; identify word-to-word correspondencesbetween each of the phrases in the first language and the correspondingcorrect word order for the phrase in the second language; generate atleast one tree that allows for the identified word-to-wordcorrespondences; based upon the at least one tree, create a statisticalmodel for reordering from a word order that is correct for the firstlanguage to a word order that is correct for the second language; andbased upon the statistical model, generate a reordered phrase from thereceived phrase, the reordered phrase having a correct word order forthe second language.
 12. The system of claim 11, wherein each at leastone tree is a binary tree.
 13. The system of claim 11, said processorfurther configured to generate a translation of the received phrase inthe second language based upon the reordered phrase.
 14. The system ofclaim 11, wherein the step of creating the statistical model furthercomprises generating a statistical model of tree generation for phrasesin the first language based upon the phrases in the corpus.
 15. Thesystem of claim 11, said input further configured to receive a requestto translate the received phrase from the first language to the secondlanguage.
 16. The system of claim 11, wherein the statistical model isgenerated using a machine learning technique.
 17. The system of claim11, wherein the machine learning technique comprises a probabilisticmachine learning technique, a quasi-Newtonian machine learningtechnique, a margin-based machine learning technique, an online machinelearning technique, or a combination thereof.
 18. The system of claim11, wherein the step of generating the at least one tree furthercomprises: generating a plurality of trees, at least one of which doesnot allow for the identified word-to-word correspondences; andselecting, from the plurality of generated trees, the at least one tree.19. The system of claim 11, wherein the word reordering informationcomprises known-good translations of the phrases in the corpus in thesecond language.